European Survey - What is the political sentiment in German speaking Countries Before Covid?
Milica Pajkic and Marco Rieder
2022-11-24
Getting the Data and installing libraries
1. Introduction
The European Social Survey (ESS) is a large scale survey conducted in over 38 countries within Europe. Focusing on public attitudes and values and changes within time. This paper is evaluating the ninth version of the survey (ESS9) form 2018.
1.1 Selection of parameters
The survey is really comprehensive and consists out of 572 variables. Some of them are related to others and only answered by a subset of participants.
1.2 Goal
2. Descriptive Statistics
## tibble [2,358 × 572] (S3: tbl_df/tbl/data.frame)
## $ dweight : num [1:2358] 0.999 0.999 0.999 0.999 0.999 ...
## $ pspwght : num [1:2358] 1.275 0.854 0.76 1.079 1.27 ...
## $ pweight : num [1:2358] 3.04 3.04 3.04 3.04 3.04 ...
## $ name : chr [1:2358] "ESS9e03_1" "ESS9e03_1" "ESS9e03_1" "ESS9e03_1" ...
## $ essround: num [1:2358] 9 9 9 9 9 9 9 9 9 9 ...
## $ edition : num [1:2358] 3.1 3.1 3.1 3.1 3.1 3.1 3.1 3.1 3.1 3.1 ...
## $ proddate: chr [1:2358] "17.02.2021" "17.02.2021" "17.02.2021" "17.02.2021" ...
## $ idno : num [1:2358] 9 10 64 65 91 119 150 212 255 270 ...
## $ cntry : chr [1:2358] "DE" "DE" "DE" "DE" ...
## $ anweight: num [1:2358] 3.87 2.59 2.31 3.28 3.86 ...
## $ prob : num [1:2358] 0.000122 0.000122 0.000122 0.000122 0.000122 ...
## $ stratum : num [1:2358] 336 284 307 338 297 323 320 295 294 278 ...
## $ psu : num [1:2358] 5856 5755 5798 5861 5779 ...
## $ nwspol : num [1:2358] 8 60 120 300 0 30 60 30 60 30 ...
## $ netusoft: num [1:2358] 5 1 3 2 5 5 4 5 4 1 ...
## $ netustm : num [1:2358] 480 6666 6666 6666 60 ...
## $ ppltrst : num [1:2358] 5 7 7 7 5 3 3 6 7 8 ...
## $ pplfair : num [1:2358] 5 8 7 6 6 6 4 5 7 9 ...
## $ pplhlp : num [1:2358] 5 5 5 3 6 5 4 5 8 9 ...
## $ polintr : num [1:2358] 3 1 1 1 3 2 1 3 2 2 ...
## $ psppsgva: num [1:2358] 4 4 2 3 2 3 3 3 2 3 ...
## $ actrolga: num [1:2358] 3 4 2 2 3 1 3 1 3 2 ...
## $ psppipla: num [1:2358] 3 4 3 2 3 8 3 2 2 3 ...
## $ cptppola: num [1:2358] 3 4 3 3 4 4 3 2 4 3 ...
## $ trstprl : num [1:2358] 2 7 3 3 4 9 10 5 5 7 ...
## $ trstlgl : num [1:2358] 4 8 5 4 5 7 10 7 8 8 ...
## $ trstplc : num [1:2358] 5 8 6 4 7 7 10 7 9 8 ...
## $ trstplt : num [1:2358] 0 6 3 3 5 8 10 5 5 6 ...
## $ trstprt : num [1:2358] 2 6 5 3 5 8 4 5 5 6 ...
## $ trstep : num [1:2358] 3 4 5 2 4 10 10 6 3 7 ...
## $ trstun : num [1:2358] 0 5 6 2 5 10 10 6 7 7 ...
## $ vote : num [1:2358] 2 1 1 1 1 1 1 1 1 1 ...
## $ prtvtcat: num [1:2358] NA NA NA NA NA NA NA NA NA NA ...
## $ prtvtdbe: num [1:2358] NA NA NA NA NA NA NA NA NA NA ...
## $ prtvtdbg: num [1:2358] NA NA NA NA NA NA NA NA NA NA ...
## $ prtvtgch: num [1:2358] NA NA NA NA NA NA NA NA NA NA ...
## $ prtvtbcy: num [1:2358] NA NA NA NA NA NA NA NA NA NA ...
## $ prtvtecz: num [1:2358] NA NA NA NA NA NA NA NA NA NA ...
## $ prtvede1: num [1:2358] 66 1 2 2 9 2 1 2 3 1 ...
## $ prtvede2: num [1:2358] 66 1 2 2 88 1 1 2 3 1 ...
## $ prtvtddk: num [1:2358] NA NA NA NA NA NA NA NA NA NA ...
## $ prtvtgee: num [1:2358] NA NA NA NA NA NA NA NA NA NA ...
## $ prtvtees: num [1:2358] NA NA NA NA NA NA NA NA NA NA ...
## $ prtvtdfi: num [1:2358] NA NA NA NA NA NA NA NA NA NA ...
## $ prtvtdfr: num [1:2358] NA NA NA NA NA NA NA NA NA NA ...
## $ prtvtcgb: num [1:2358] NA NA NA NA NA NA NA NA NA NA ...
## $ prtvtahr: num [1:2358] NA NA NA NA NA NA NA NA NA NA ...
## $ prtvtfhu: num [1:2358] NA NA NA NA NA NA NA NA NA NA ...
## $ prtvtcie: num [1:2358] NA NA NA NA NA NA NA NA NA NA ...
## $ prtvtcis: num [1:2358] NA NA NA NA NA NA NA NA NA NA ...
## $ prtvtcit: num [1:2358] NA NA NA NA NA NA NA NA NA NA ...
## $ prtvblt1: num [1:2358] NA NA NA NA NA NA NA NA NA NA ...
## $ prtvblt2: num [1:2358] NA NA NA NA NA NA NA NA NA NA ...
## $ prtvblt3: num [1:2358] NA NA NA NA NA NA NA NA NA NA ...
## $ prtvtalv: num [1:2358] NA NA NA NA NA NA NA NA NA NA ...
## $ prtvtme : num [1:2358] NA NA NA NA NA NA NA NA NA NA ...
## $ prtvtgnl: num [1:2358] NA NA NA NA NA NA NA NA NA NA ...
## $ prtvtbno: num [1:2358] NA NA NA NA NA NA NA NA NA NA ...
## $ prtvtdpl: num [1:2358] NA NA NA NA NA NA NA NA NA NA ...
## $ prtvtcpt: num [1:2358] NA NA NA NA NA NA NA NA NA NA ...
## $ prtvtrs : num [1:2358] NA NA NA NA NA NA NA NA NA NA ...
## $ prtvtcse: num [1:2358] NA NA NA NA NA NA NA NA NA NA ...
## $ prtvtfsi: num [1:2358] NA NA NA NA NA NA NA NA NA NA ...
## $ prtvtdsk: num [1:2358] NA NA NA NA NA NA NA NA NA NA ...
## $ contplt : num [1:2358] 2 2 2 1 2 2 1 2 2 1 ...
## $ wrkprty : num [1:2358] 2 2 2 2 2 2 2 2 2 1 ...
## $ wrkorg : num [1:2358] 1 2 1 1 1 2 1 2 2 2 ...
## $ badge : num [1:2358] 2 2 2 2 2 2 2 2 2 2 ...
## $ sgnptit : num [1:2358] 2 2 2 2 1 2 1 2 1 2 ...
## $ pbldmn : num [1:2358] 2 1 2 2 2 2 2 2 1 2 ...
## $ bctprd : num [1:2358] 2 1 2 1 1 2 1 2 1 2 ...
## $ pstplonl: num [1:2358] 2 2 2 2 1 2 2 2 2 2 ...
## $ clsprty : num [1:2358] 2 1 1 1 2 2 1 1 1 1 ...
## $ prtcldat: num [1:2358] NA NA NA NA NA NA NA NA NA NA ...
## $ prtcldbe: num [1:2358] NA NA NA NA NA NA NA NA NA NA ...
## $ prtcldbg: num [1:2358] NA NA NA NA NA NA NA NA NA NA ...
## $ prtclgch: num [1:2358] NA NA NA NA NA NA NA NA NA NA ...
## $ prtclbcy: num [1:2358] NA NA NA NA NA NA NA NA NA NA ...
## $ prtclecz: num [1:2358] NA NA NA NA NA NA NA NA NA NA ...
## $ prtclede: num [1:2358] 66 1 2 2 66 66 1 2 3 1 ...
## $ prtclddk: num [1:2358] NA NA NA NA NA NA NA NA NA NA ...
## $ prtclgee: num [1:2358] NA NA NA NA NA NA NA NA NA NA ...
## $ prtclfes: num [1:2358] NA NA NA NA NA NA NA NA NA NA ...
## $ prtclefi: num [1:2358] NA NA NA NA NA NA NA NA NA NA ...
## $ prtclffr: num [1:2358] NA NA NA NA NA NA NA NA NA NA ...
## $ prtclcgb: num [1:2358] NA NA NA NA NA NA NA NA NA NA ...
## $ prtclahr: num [1:2358] NA NA NA NA NA NA NA NA NA NA ...
## $ prtclghu: num [1:2358] NA NA NA NA NA NA NA NA NA NA ...
## $ prtcleie: num [1:2358] NA NA NA NA NA NA NA NA NA NA ...
## $ prtclcis: num [1:2358] NA NA NA NA NA NA NA NA NA NA ...
## $ prtcldit: num [1:2358] NA NA NA NA NA NA NA NA NA NA ...
## $ prtclblt: num [1:2358] NA NA NA NA NA NA NA NA NA NA ...
## $ prtclalv: num [1:2358] NA NA NA NA NA NA NA NA NA NA ...
## $ prtclme : num [1:2358] NA NA NA NA NA NA NA NA NA NA ...
## $ prtclfnl: num [1:2358] NA NA NA NA NA NA NA NA NA NA ...
## $ prtclbno: num [1:2358] NA NA NA NA NA NA NA NA NA NA ...
## $ prtclhpl: num [1:2358] NA NA NA NA NA NA NA NA NA NA ...
## $ prtclept: num [1:2358] NA NA NA NA NA NA NA NA NA NA ...
## $ prtclrs : num [1:2358] NA NA NA NA NA NA NA NA NA NA ...
## [list output truncated]2.1 Cleaning of the data
## 1 2 3 NA's
## 22 1902 136 298## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## 0 1700 2800 9931 4500 200000 1235## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## 0 19494 33600 38700 50000 288000 12353. Models
3.1 Linear Model
Question: What is an influence on the
## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## 0.00 30.00 45.00 62.98 75.00 1200.00 1## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## 0.00 12.00 14.00 14.29 16.00 30.00 7## 1 2
## 1212 1146## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## 15.00 34.00 51.00 49.65 64.00 90.00 4## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## 0.000 4.000 6.000 5.887 8.000 10.000 37## 0 1 2 3 4 5 6 7 8 9 10 NA's
## 56 45 119 206 214 285 297 422 424 177 76 37## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## 1.000 2.000 3.000 2.789 3.000 5.000 18## not at all a little quite very completely NA's
## 224 755 802 408 151 18## none at all hardly quite very NA's
## 100 691 997 569 1## 1 2 3 4 NA's
## 569 997 691 100 1Graphical analysis
Fitting Model
##
## Call:
## lm(formula = nwspol.na ~ eduyrs.na, data = ess9_linear)
##
## Residuals:
## Min 1Q Median 3Q Max
## -65.26 -34.53 -16.98 11.20 1136.20
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 66.9534 7.1991 9.300 <0.0000000000000002 ***
## eduyrs.na -0.2424 0.4876 -0.497 0.619
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 81.35 on 2294 degrees of freedom
## Multiple R-squared: 0.0001077, Adjusted R-squared: -0.0003281
## F-statistic: 0.2472 on 1 and 2294 DF, p-value: 0.6191## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 66.9534190 7.1990896 9.3002620 0.00000000000000000003161288
## eduyrs.na -0.2424129 0.4875839 -0.4971717 0.61911565218440234303898251##
## Call:
## lm(formula = nwspol.na ~ age.na, data = ess9_linear)
##
## Residuals:
## Min 1Q Median 3Q Max
## -92.23 -34.89 -15.36 13.66 1119.49
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 15.09729 4.62268 3.266 0.00111 **
## age.na 0.97636 0.08713 11.206 < 0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 79.21 on 2294 degrees of freedom
## Multiple R-squared: 0.0519, Adjusted R-squared: 0.05149
## F-statistic: 125.6 on 1 and 2294 DF, p-value: < 0.00000000000000022
Interpretation: we can see that the line is nearly flat and the p-value confirms this observation. The p-Value is nearly 1 and the parameter is nearly 0.
##
## Call:
## lm(formula = nwspol.outlier ~ eduyrs.na, data = ess9_de, na.omit = TRUE)
##
## Residuals:
## Min 1Q Median 3Q Max
## -59.30 -29.10 -12.57 11.57 302.81
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 55.2679 4.3602 12.675 <0.0000000000000002 ***
## eduyrs.na 0.1918 0.2962 0.647 0.517
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 50.05 on 2333 degrees of freedom
## (23 Beobachtungen als fehlend gelöscht)
## Multiple R-squared: 0.0001797, Adjusted R-squared: -0.0002489
## F-statistic: 0.4192 on 1 and 2333 DF, p-value: 0.5174Adding additional predictors
##
## Call:
## lm(formula = nwspol.na ~ age.na + eduyrs.na, data = ess9_linear)
##
## Residuals:
## Min 1Q Median 3Q Max
## -91.77 -34.82 -15.19 13.78 1119.62
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 13.35443 8.49027 1.573 0.116
## age.na 0.97780 0.08734 11.195 <0.0000000000000002 ***
## eduyrs.na 0.11649 0.47597 0.245 0.807
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 79.23 on 2293 degrees of freedom
## Multiple R-squared: 0.05193, Adjusted R-squared: 0.0511
## F-statistic: 62.8 on 2 and 2293 DF, p-value: < 0.00000000000000022## (Intercept) age.na eduyrs.na
## 13.3544308 0.9778000 0.1164948## Estimate Std. Error t value
## (Intercept) 13.3544308 8.49026594 1.5729108
## age.na 0.9778000 0.08734175 11.1951044
## eduyrs.na 0.1164948 0.47596568 0.2447547
## Pr(>|t|)
## (Intercept) 0.1158774401912398832603656728679198
## age.na 0.0000000000000000000000000002309964
## eduyrs.na 0.8066682762931414174545352580025792## 2.5 % 97.5 %
## (Intercept) -3.2949730 30.003835
## age.na 0.8065229 1.149077
## eduyrs.na -0.8168734 1.049863We can see that the predictor age has a low p-value and it is significant.
##
## Call:
## lm(formula = nwspol.na ~ eduyrs.na + age.na + gndr.fac + stfdem.na +
## ability.fac + interest.fac, data = ess9_linear)
##
## Residuals:
## Min 1Q Median 3Q Max
## -100.69 -31.90 -12.93 13.30 1127.04
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 46.82360 11.01789 4.250 0.0000222574534076 ***
## eduyrs.na -0.96445 0.49499 -1.948 0.05149 .
## age.na 0.71236 0.09329 7.636 0.0000000000000328 ***
## gndr.fac2 -2.69782 3.31452 -0.814 0.41576
## stfdem.na -1.03706 0.70387 -1.473 0.14079
## ability.faca little -2.29699 6.31408 -0.364 0.71605
## ability.facquite -0.58392 6.55242 -0.089 0.92900
## ability.facvery 2.89396 7.29519 0.397 0.69163
## ability.faccompletely 14.09631 8.93151 1.578 0.11464
## interest.fac.L 29.65914 6.75701 4.389 0.0000118846240809 ***
## interest.fac.Q 13.83210 5.04290 2.743 0.00614 **
## interest.fac.C -2.64438 3.33380 -0.793 0.42774
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 77.76 on 2284 degrees of freedom
## Multiple R-squared: 0.09036, Adjusted R-squared: 0.08598
## F-statistic: 20.62 on 11 and 2284 DF, p-value: < 0.00000000000000022## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 46.8235974 11.01788591 4.24978056 0.00002225745340756224
## eduyrs.na -0.9644454 0.49499357 -1.94839985 0.05148945434962375750
## age.na 0.7123618 0.09329339 7.63571519 0.00000000000003278539
## gndr.fac2 -2.6978177 3.31451767 -0.81393977 0.41576430595330315931
## stfdem.na -1.0370582 0.70386803 -1.47337019 0.14078903977644036116
## ability.faca little -2.2969899 6.31407706 -0.36378870 0.71604947094609150415
## ability.facquite -0.5839200 6.55241874 -0.08911518 0.92899818723781546481
## ability.facvery 2.8939586 7.29518851 0.39669415 0.69163007455917546729
## ability.faccompletely 14.0963060 8.93151086 1.57826668 0.11464283451138868042
## interest.fac.L 29.6591412 6.75701149 4.38938741 0.00001188462408086408
## interest.fac.Q 13.8320989 5.04290452 2.74288336 0.00613777558879450603
## interest.fac.C -2.6443798 3.33379622 -0.79320378 0.42774149293002872163## 2.5 % 97.5 %
## (Intercept) 25.2174882 68.429706625
## eduyrs.na -1.9351294 0.006238564
## age.na 0.5294131 0.895310385
## gndr.fac2 -9.1975974 3.801961940
## stfdem.na -2.4173456 0.343229267
## ability.faca little -14.6789151 10.084935267
## ability.facquite -13.4332339 12.265393970
## ability.facvery -11.4119292 17.199846419
## ability.faccompletely -3.4184151 31.611027139
## interest.fac.L 16.4086202 42.909662132
## interest.fac.Q 3.9429471 23.721250676
## interest.fac.C -9.1819647 3.893205185
Interaction: Age * Interest
##
## Call:
## lm(formula = nwspol.na ~ eduyrs.na + age.na * interest.fac +
## gndr.fac + stfdem.na + ability.fac, data = ess9_linear)
##
## Residuals:
## Min 1Q Median 3Q Max
## -105.53 -31.57 -13.78 13.58 1124.69
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 47.2447 11.7517 4.020 0.000060026 ***
## eduyrs.na -0.9064 0.4954 -1.830 0.0674 .
## age.na 0.6758 0.1302 5.191 0.000000228 ***
## interest.fac.L 9.1358 15.9051 0.574 0.5658
## interest.fac.Q 7.4665 12.7089 0.587 0.5569
## interest.fac.C 6.2965 8.7081 0.723 0.4697
## gndr.fac2 -2.4274 3.3147 -0.732 0.4641
## stfdem.na -1.0906 0.7037 -1.550 0.1213
## ability.faca little -3.6725 6.3316 -0.580 0.5620
## ability.facquite -1.4807 6.5698 -0.225 0.8217
## ability.facvery 2.7415 7.2937 0.376 0.7070
## ability.faccompletely 13.8350 8.9299 1.549 0.1215
## age.na:interest.fac.L 0.4165 0.3182 1.309 0.1906
## age.na:interest.fac.Q 0.1103 0.2566 0.430 0.6673
## age.na:interest.fac.C -0.2110 0.1758 -1.201 0.2301
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 77.69 on 2281 degrees of freedom
## Multiple R-squared: 0.09316, Adjusted R-squared: 0.08759
## F-statistic: 16.74 on 14 and 2281 DF, p-value: < 0.00000000000000022## (Intercept) eduyrs.na age.na
## 47.2447097 -0.9064125 0.6758316
## interest.fac.L interest.fac.Q interest.fac.C
## 9.1357745 7.4664644 6.2965187
## gndr.fac2 stfdem.na ability.faca little
## -2.4274402 -1.0905985 -3.6724999
## ability.facquite ability.facvery ability.faccompletely
## -1.4807135 2.7415278 13.8349518
## age.na:interest.fac.L age.na:interest.fac.Q age.na:interest.fac.C
## 0.4165108 0.1103203 -0.2110107## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 47.2447097 11.7517104 4.0202411 0.0000600263264
## eduyrs.na -0.9064125 0.4954314 -1.8295417 0.0674489633340
## age.na 0.6758316 0.1301902 5.1911105 0.0000002275338
## interest.fac.L 9.1357745 15.9050505 0.5743946 0.5657574699023
## interest.fac.Q 7.4664644 12.7089193 0.5874980 0.5569274601306
## interest.fac.C 6.2965187 8.7080929 0.7230652 0.4697139785251
## gndr.fac2 -2.4274402 3.3147418 -0.7323165 0.4640506195895
## stfdem.na -1.0905985 0.7036944 -1.5498183 0.1213238491175
## ability.faca little -3.6724999 6.3316232 -0.5800250 0.5619550348906
## ability.facquite -1.4807135 6.5697715 -0.2253828 0.8217016811062
## ability.facvery 2.7415278 7.2937490 0.3758736 0.7070458104483
## ability.faccompletely 13.8349518 8.9299356 1.5492779 0.1214536545791
## age.na:interest.fac.L 0.4165108 0.3181606 1.3091212 0.1906251586660
## age.na:interest.fac.Q 0.1103203 0.2566211 0.4298956 0.6673121709087
## age.na:interest.fac.C -0.2110107 0.1757617 -1.2005499 0.2300505141922## 2.5 % 97.5 %
## (Intercept) 24.1995523 70.28986719
## eduyrs.na -1.8779558 0.06513082
## age.na 0.4205281 0.93113517
## interest.fac.L -22.0541017 40.32565078
## interest.fac.Q -17.4557841 32.38871283
## interest.fac.C -10.7800910 23.37312831
## gndr.fac2 -8.9276639 4.07278341
## stfdem.na -2.4705464 0.28934943
## ability.faca little -16.0888417 8.74384199
## ability.facquite -14.3640653 11.40263833
## ability.facvery -11.5615471 17.04460271
## ability.faccompletely -3.6766924 31.34659597
## age.na:interest.fac.L -0.2074036 1.04042509
## age.na:interest.fac.Q -0.3929149 0.61355545
## age.na:interest.fac.C -0.5556802 0.13365880
The interaction is not significant
Testing several Variables
## nwspol.na ~ age.na + eduyrs.na## nwspol.na ~ eduyrs.na + age.na + gndr.fac + stfdem.na + ability.fac +
## interest.fac## nwspol.na ~ eduyrs.na + age.na * interest.fac + gndr.fac + stfdem.na +
## ability.fac## nwspol.na ~ eduyrs.na + age.na + gndr.fac + stfdem.na + ability.fac +
## interest.fac## Single term deletions
##
## Model:
## nwspol.na ~ eduyrs.na + age.na + gndr.fac + stfdem.na + ability.fac +
## interest.fac
## Df Sum of Sq RSS AIC F value Pr(>F)
## <none> 13809380 20004
## eduyrs.na 1 22953 13832333 20006 3.7963 0.05149 .
## age.na 1 352515 14161895 20060 58.3041 0.00000000000003279 ***
## gndr.fac 1 4006 13813386 20002 0.6625 0.41576
## stfdem.na 1 13125 13822505 20004 2.1708 0.14079
## ability.fac 4 33085 13842466 20001 1.3680 0.24256
## interest.fac 3 404127 14213507 20064 22.2802 0.00000000000003227 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Fitted Values and Residuals
Fitted Values
## Named num [1:2296] 63.8 64.3 62.1 64 63.6 ...
## - attr(*, "names")= chr [1:2296] "1" "2" "3" "4" ...## 1 2 3 4 5 6
## 63.80205 64.28688 62.10516 64.04446 63.55964 63.80205
There are 67 unique values, therefore, the other observations are overlapping. Here we zoomed in to see the negative relationship between news consumption and education years.
Residuals
## [1] 2296## 1 2 3 4 5 6
## -55.802052 -4.286877 57.894839 235.955536 -63.559639 -33.802052## 166 1215 1899 1912 1666 1890 541
## -3.559639 -42.105161 -19.286877 -33.317226 -4.044464 6.440361 -49.044464
## 990 1837 511 127 323 957 633
## 415.470710 -2.832400 -59.317226 -32.832400 -2.347574 -33.559639 -3.317226
## 456 1983 180 969 981 465
## 57.652426 -34.529290 -18.317226 -2.347574 -47.105161 85.955536## 166 1215 1899 1912 1666 1890 541 990
## 63.55964 62.10516 64.28688 63.31723 64.04446 63.55964 64.04446 64.52929
## 1837 511 127 323 957 633 456 1983
## 62.83240 63.31723 62.83240 62.34757 63.55964 63.31723 62.34757 64.52929
## 180 969 981 465
## 63.31723 62.34757 62.10516 64.04446
Predicted values
## fit lwr upr
## 1 62.58999 -97.00055 222.1805
## 2 65.25653 -94.45044 224.9635
## 3 60.16586 -99.91952 220.2512
3.2 GLM Poisson
The goal of this chapter is to apply a generalized linear model of the poisson type on the ESS9 data set. The poisson model is applied to count data, in this case the information of how many minutes the participants spend per day for consuming media, such as newspaper, TV new shows or online resources. In this chapter the model is used to simulate the data, based on the provided data from the survey. First the data is cleaned and prepared for using it. A subset of the data is used, containing variables which should help to model the news consumption of a person. These parameters are used: Depended variable:
- How much politics are you watching (“nwspol”) Independent variables:
The goal of this chapter is to apply a generalized linear model of the poisson type on the ESS9 data set. The poisson model is applied to count data, in this case the information of how many minutes the participants spend per day for consuming media, such as newspaper, TV new shows or online resources. In this chapter the model is used to simulate the data, based on the provided data from the survey. First the data is cleaned and prepared for using it. A subset of the data is used, containing variables which should help to model the news consumption of a person. These parameters are used: Depended variable:
How much politics are you watching (“nwspol”) Independent variables:
Interest in politics (“polintr)
Trust into the current parlament (“trstprl”)
Highest level of education (“eisced”)
Years of education (“eduyrs”)
Satisfaction with the general economical situation (“stfeco”)
Satisfaction with the current government (“stfgov”)
Gender (“gndr”)
Age (“agea”)
Level of person religion believe (“rlgdgr”)
Time spent online (“netusoft”)
Posibility for political participation (“psppsgva”)
Yearly gross income (combination of two factors into a new one - “yrpy”)
Interest in politics (“polintr)
Trust into the current parlament (“trstprl”)
Highest level of education (“eisced”)
Years of education (“eduyrs”)
Satisfaction with the general economical situation (“stfeco”)
Satisfaction with the current government (“stfgov”)
Gender (“gndr”)
Age (“agea”)
Level of person religion believe (“rlgdgr”)
Time spent online (“netusoft”)
Posibility for political participation (“psppsgva”)
Yearly gross income (combination of two factors into a new one - “yrpy”)
3.2.1 Data preparation
The data have to be prepared and transformed. For poisson count data are needed for the predictive variable. The time in minutes is modeled therefore.
## [1] 572## [1] 708## [1] 1752## [1] 47Subset of data for this task, as 14 parameters in total. The rows with NA are dropped, as the data set is large enough for droping theses values.
## tibble [17,029 × 14] (S3: tbl_df/tbl/data.frame)
## $ nwspol : num [1:17029] 60 45 60 120 15 60 30 30 10 10 ...
## $ polintr : Factor w/ 4 levels "1","2","3","4": 4 3 4 2 4 1 2 3 2 2 ...
## $ trstprl : Factor w/ 11 levels "0","1","2","3",..: 7 1 1 7 5 4 4 8 8 6 ...
## $ eisced : Factor w/ 8 levels "1","2","3","4",..: 2 3 3 3 3 4 3 6 2 7 ...
## $ eduyrs : num [1:17029] 12 11 12 12 13 21 18 17 9 17 ...
## $ stfeco : Factor w/ 11 levels "0","1","2","3",..: 6 7 2 11 10 8 7 8 7 9 ...
## $ stfgov : Factor w/ 11 levels "0","1","2","3",..: 7 9 4 11 9 3 8 3 8 7 ...
## $ stfdem : Factor w/ 11 levels "0","1","2","3",..: 7 7 4 11 8 4 11 7 9 8 ...
## $ gndr : Factor w/ 2 levels "1","2": 2 1 1 1 2 1 1 1 1 1 ...
## $ agea : num [1:17029] 40 63 56 48 41 27 49 42 50 35 ...
## $ rlgdgr : Factor w/ 11 levels "0","1","2","3",..: 5 2 9 1 4 4 3 1 4 3 ...
## $ netusoft: Factor w/ 5 levels "1","2","3","4",..: 4 5 1 1 4 5 5 5 5 5 ...
## $ psppsgva: Factor w/ 5 levels "1","2","3","4",..: 2 2 2 5 1 3 1 1 2 3 ...
## $ yrpy : num [1:17029] 31200 30600 18000 31200 37200 18000 20400 17400 45600 70000 ...The plot shows the media consumption for male and female. Males have a higher mean and the variance of the first and thrid quantile are larger.
The plot shows the media consumption for male and female. Males have a higher mean and the variance of the first and third quantile are larger.
3.2.2 Fitting the poisson model
Using the parameter specified above to model the consumption.
##
## Call:
## glm(formula = nwspol ~ polintr + eisced + trstprl + eduyrs +
## netusoft + stfeco + stfgov + stfdem + gndr + agea + rlgdgr +
## yrpy, family = "poisson", data = ess9_poisson)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -17.160 -6.476 -3.322 0.474 74.725
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 4.9551478455834 0.0105343674796 470.379 < 0.0000000000000002 ***
## polintr2 -0.3492665558265 0.0026678349780 -130.918 < 0.0000000000000002 ***
## polintr3 -0.5376726514740 0.0028816344085 -186.586 < 0.0000000000000002 ***
## polintr4 -0.6563093286586 0.0037354537637 -175.697 < 0.0000000000000002 ***
## eisced2 -0.1458105762706 0.0064736859414 -22.524 < 0.0000000000000002 ***
## eisced3 -0.2098897454780 0.0063900096516 -32.847 < 0.0000000000000002 ***
## eisced4 -0.0801180098272 0.0062959701053 -12.725 < 0.0000000000000002 ***
## eisced5 -0.1793751705951 0.0066035938535 -27.163 < 0.0000000000000002 ***
## eisced6 -0.0136582683597 0.0068047744981 -2.007 0.0447 *
## eisced7 -0.0747622892396 0.0070310740541 -10.633 < 0.0000000000000002 ***
## eisced55 0.1991819464428 0.0184339911655 10.805 < 0.0000000000000002 ***
## trstprl1 0.0438116718767 0.0056421385757 7.765 0.000000000000008159 ***
## trstprl2 0.0878787242680 0.0050348881462 17.454 < 0.0000000000000002 ***
## trstprl3 0.0575992821977 0.0049226015149 11.701 < 0.0000000000000002 ***
## trstprl4 0.1649699717288 0.0049617090096 33.249 < 0.0000000000000002 ***
## trstprl5 0.1785393616823 0.0046962221481 38.018 < 0.0000000000000002 ***
## trstprl6 0.0926411783091 0.0050274726710 18.427 < 0.0000000000000002 ***
## trstprl7 0.1654323040088 0.0050166399191 32.977 < 0.0000000000000002 ***
## trstprl8 0.2017014330997 0.0053305193535 37.839 < 0.0000000000000002 ***
## trstprl9 0.1425488919881 0.0068020761893 20.957 < 0.0000000000000002 ***
## trstprl10 0.1034979576788 0.0080156227526 12.912 < 0.0000000000000002 ***
## eduyrs -0.0047838263191 0.0003248431772 -14.727 < 0.0000000000000002 ***
## netusoft2 -0.1307787280215 0.0062823662011 -20.817 < 0.0000000000000002 ***
## netusoft3 -0.1124713541777 0.0061051654140 -18.422 < 0.0000000000000002 ***
## netusoft4 -0.1650624943097 0.0053911538839 -30.617 < 0.0000000000000002 ***
## netusoft5 -0.2565499520473 0.0047119730917 -54.446 < 0.0000000000000002 ***
## stfeco1 0.2042630443847 0.0078684572864 25.960 < 0.0000000000000002 ***
## stfeco2 0.1454844795976 0.0065882376682 22.082 < 0.0000000000000002 ***
## stfeco3 0.1097594146381 0.0063537352177 17.275 < 0.0000000000000002 ***
## stfeco4 0.1173993792901 0.0063956760982 18.356 < 0.0000000000000002 ***
## stfeco5 0.1060677186837 0.0062503682707 16.970 < 0.0000000000000002 ***
## stfeco6 0.0454950862456 0.0063907869403 7.119 0.000000000001088272 ***
## stfeco7 -0.0695855130540 0.0063960463742 -10.879 < 0.0000000000000002 ***
## stfeco8 -0.0844850487183 0.0065231751269 -12.952 < 0.0000000000000002 ***
## stfeco9 -0.1059951308680 0.0073197601880 -14.481 < 0.0000000000000002 ***
## stfeco10 -0.2077250234061 0.0087105452425 -23.848 < 0.0000000000000002 ***
## stfgov1 -0.0371282966740 0.0053909882570 -6.887 0.000000000005693961 ***
## stfgov2 -0.1628748230687 0.0050296948069 -32.383 < 0.0000000000000002 ***
## stfgov3 -0.1492579265587 0.0049582011543 -30.103 < 0.0000000000000002 ***
## stfgov4 -0.0842706452037 0.0050657063724 -16.636 < 0.0000000000000002 ***
## stfgov5 -0.0532296925917 0.0049390418045 -10.777 < 0.0000000000000002 ***
## stfgov6 -0.1646096319626 0.0052200210811 -31.534 < 0.0000000000000002 ***
## stfgov7 -0.0601493306535 0.0052887394475 -11.373 < 0.0000000000000002 ***
## stfgov8 -0.1257885537272 0.0059854927793 -21.016 < 0.0000000000000002 ***
## stfgov9 0.1018727906473 0.0080519498858 12.652 < 0.0000000000000002 ***
## stfgov10 0.0782407518655 0.0098315985238 7.958 0.000000000000001747 ***
## stfdem1 -0.0563098592349 0.0069113969117 -8.147 0.000000000000000372 ***
## stfdem2 -0.0025138003015 0.0061045169098 -0.412 0.6805
## stfdem3 -0.1749548063588 0.0060699082915 -28.823 < 0.0000000000000002 ***
## stfdem4 -0.0834553121230 0.0061336706557 -13.606 < 0.0000000000000002 ***
## stfdem5 -0.0745510981878 0.0059070855389 -12.621 < 0.0000000000000002 ***
## stfdem6 -0.1142834927352 0.0061219691225 -18.668 < 0.0000000000000002 ***
## stfdem7 -0.0757921344766 0.0060426970153 -12.543 < 0.0000000000000002 ***
## stfdem8 -0.1281854636406 0.0062024480020 -20.667 < 0.0000000000000002 ***
## stfdem9 -0.1819244111818 0.0069479462167 -26.184 < 0.0000000000000002 ***
## stfdem10 -0.1081689324763 0.0079802880965 -13.555 < 0.0000000000000002 ***
## gndr2 -0.0955221839468 0.0018928147422 -50.466 < 0.0000000000000002 ***
## agea 0.0039442838404 0.0000752630756 52.407 < 0.0000000000000002 ***
## rlgdgr1 0.0524594264550 0.0039129662333 13.407 < 0.0000000000000002 ***
## rlgdgr2 0.0229590182679 0.0037164081451 6.178 0.000000000650240197 ***
## rlgdgr3 0.1252369296839 0.0035904157083 34.881 < 0.0000000000000002 ***
## rlgdgr4 0.0911051329206 0.0040471525705 22.511 < 0.0000000000000002 ***
## rlgdgr5 0.0008947887099 0.0032698289371 0.274 0.7844
## rlgdgr6 0.0599344542248 0.0035893248803 16.698 < 0.0000000000000002 ***
## rlgdgr7 -0.0077316837152 0.0035782505209 -2.161 0.0307 *
## rlgdgr8 0.0079295142834 0.0038527509859 2.058 0.0396 *
## rlgdgr9 -0.0750978361239 0.0059677036213 -12.584 < 0.0000000000000002 ***
## rlgdgr10 0.1758154007801 0.0047371153192 37.114 < 0.0000000000000002 ***
## yrpy -0.0000000085380 0.0000000005367 -15.907 < 0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for poisson family taken to be 1)
##
## Null deviance: 1622523 on 17028 degrees of freedom
## Residual deviance: 1532064 on 16960 degrees of freedom
## AIC: 1622455
##
## Number of Fisher Scoring iterations: 6## (Intercept) polintr2 polintr3 polintr4
## 4.955147845583398 -0.349266555826538 -0.537672651474009 -0.656309328658579
## eisced2 eisced3 eisced4 eisced5
## -0.145810576270611 -0.209889745477968 -0.080118009827152 -0.179375170595105
## eisced6 eisced7 eisced55 trstprl1
## -0.013658268359742 -0.074762289239633 0.199181946442816 0.043811671876679
## trstprl2 trstprl3 trstprl4 trstprl5
## 0.087878724267998 0.057599282197725 0.164969971728847 0.178539361682290
## trstprl6 trstprl7 trstprl8 trstprl9
## 0.092641178309068 0.165432304008800 0.201701433099665 0.142548891988131
## trstprl10 eduyrs netusoft2 netusoft3
## 0.103497957678787 -0.004783826319139 -0.130778728021462 -0.112471354177693
## netusoft4 netusoft5 stfeco1 stfeco2
## -0.165062494309659 -0.256549952047336 0.204263044384658 0.145484479597629
## stfeco3 stfeco4 stfeco5 stfeco6
## 0.109759414638132 0.117399379290054 0.106067718683748 0.045495086245550
## stfeco7 stfeco8 stfeco9 stfeco10
## -0.069585513054008 -0.084485048718322 -0.105995130868024 -0.207725023406075
## stfgov1 stfgov2 stfgov3 stfgov4
## -0.037128296673999 -0.162874823068651 -0.149257926558705 -0.084270645203717
## stfgov5 stfgov6 stfgov7 stfgov8
## -0.053229692591687 -0.164609631962625 -0.060149330653520 -0.125788553727176
## stfgov9 stfgov10 stfdem1 stfdem2
## 0.101872790647289 0.078240751865529 -0.056309859234943 -0.002513800301504
## stfdem3 stfdem4 stfdem5 stfdem6
## -0.174954806358804 -0.083455312122970 -0.074551098187797 -0.114283492735243
## stfdem7 stfdem8 stfdem9 stfdem10
## -0.075792134476610 -0.128185463640629 -0.181924411181762 -0.108168932476321
## gndr2 agea rlgdgr1 rlgdgr2
## -0.095522183946788 0.003944283840447 0.052459426455015 0.022959018267916
## rlgdgr3 rlgdgr4 rlgdgr5 rlgdgr6
## 0.125236929683865 0.091105132920631 0.000894788709914 0.059934454224848
## rlgdgr7 rlgdgr8 rlgdgr9 rlgdgr10
## -0.007731683715226 0.007929514283363 -0.075097836123885 0.175815400780054
## yrpy
## -0.000000008538016## (Intercept)
## 141.90363.2.3 Simulation
Now we simulate with our model the average media consumption for Females and Males.
## [1] 17029## sim_1
## 1 48
## 2 47
## 3 53
## 4 81
## 5 44
## 6 79
The simulated data look similar as the real data based of the survey. The variance is smaller for the simulated one. The higher mean for males are in both plots visible.
The simulated data look similar as the real data based of the survey. The variance is smaller for the simulated one. The higher mean and the higher variance for the male group is indicated in the real data as well as in the simulated data.
3.2.4 GLM Quasi-Poisson
##
## Call:
## glm(formula = nwspol ~ polintr + eisced + trstprl + eduyrs +
## netusoft + stfeco + stfgov + stfdem + gndr + agea + rlgdgr +
## yrpy, family = "quasipoisson", data = ess9_poisson)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -17.160 -6.476 -3.322 0.474 74.725
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4.955147845583 0.151318671203 32.746 < 0.0000000000000002 ***
## polintr2 -0.349266555827 0.038321545611 -9.114 < 0.0000000000000002 ***
## polintr3 -0.537672651474 0.041392621858 -12.990 < 0.0000000000000002 ***
## polintr4 -0.656309328659 0.053657127583 -12.232 < 0.0000000000000002 ***
## eisced2 -0.145810576271 0.092989878732 -1.568 0.116894
## eisced3 -0.209889745478 0.091787928543 -2.287 0.022227 *
## eisced4 -0.080118009827 0.090437117569 -0.886 0.375685
## eisced5 -0.179375170595 0.094855913182 -1.891 0.058638 .
## eisced6 -0.013658268360 0.097745729573 -0.140 0.888873
## eisced7 -0.074762289240 0.100996361201 -0.740 0.459160
## eisced55 0.199181946443 0.264791128042 0.752 0.451927
## trstprl1 0.043811671877 0.081045294240 0.541 0.588802
## trstprl2 0.087878724268 0.072322575173 1.215 0.224347
## trstprl3 0.057599282198 0.070709657845 0.815 0.415319
## trstprl4 0.164969971729 0.071271409098 2.315 0.020643 *
## trstprl5 0.178539361682 0.067457879792 2.647 0.008136 **
## trstprl6 0.092641178309 0.072216057163 1.283 0.199568
## trstprl7 0.165432304009 0.072060452413 2.296 0.021703 *
## trstprl8 0.201701433100 0.076569106494 2.634 0.008440 **
## trstprl9 0.142548891988 0.097706970293 1.459 0.144599
## trstprl10 0.103497957679 0.115138700652 0.899 0.368720
## eduyrs -0.004783826319 0.004666140422 -1.025 0.305273
## netusoft2 -0.130778728021 0.090241707193 -1.449 0.147299
## netusoft3 -0.112471354178 0.087696344343 -1.283 0.199682
## netusoft4 -0.165062494310 0.077440078253 -2.131 0.033063 *
## netusoft5 -0.256549952047 0.067684130856 -3.790 0.000151 ***
## stfeco1 0.204263044385 0.113024773751 1.807 0.070742 .
## stfeco2 0.145484479598 0.094635332540 1.537 0.124234
## stfeco3 0.109759414638 0.091266872185 1.203 0.229140
## stfeco4 0.117399379290 0.091869323004 1.278 0.201304
## stfeco5 0.106067718684 0.089782079758 1.181 0.237464
## stfeco6 0.045495086246 0.091799093740 0.496 0.620187
## stfeco7 -0.069585513054 0.091874641754 -0.757 0.448823
## stfeco8 -0.084485048718 0.093700755564 -0.902 0.367257
## stfeco9 -0.105995130868 0.105143131500 -1.008 0.313419
## stfeco10 -0.207725023406 0.125120766301 -1.660 0.096893 .
## stfgov1 -0.037128296674 0.077437699141 -0.479 0.631617
## stfgov2 -0.162874823069 0.072247976560 -2.254 0.024185 *
## stfgov3 -0.149257926559 0.071221021260 -2.096 0.036124 *
## stfgov4 -0.084270645204 0.072765256999 -1.158 0.246833
## stfgov5 -0.053229692592 0.070945810873 -0.750 0.453093
## stfgov6 -0.164609631963 0.074981877665 -2.195 0.028154 *
## stfgov7 -0.060149330654 0.075968967960 -0.792 0.428511
## stfgov8 -0.125788553727 0.085977332347 -1.463 0.143474
## stfgov9 0.101872790647 0.115660513996 0.881 0.378443
## stfgov10 0.078240751866 0.141223896670 0.554 0.579573
## stfdem1 -0.056309859235 0.099277284456 -0.567 0.570587
## stfdem2 -0.002513800302 0.087687029042 -0.029 0.977130
## stfdem3 -0.174954806359 0.087189900939 -2.007 0.044809 *
## stfdem4 -0.083455312123 0.088105801797 -0.947 0.343542
## stfdem5 -0.074551098188 0.084851068293 -0.879 0.379625
## stfdem6 -0.114283492735 0.087937717623 -1.300 0.193757
## stfdem7 -0.075792134477 0.086799030374 -0.873 0.382571
## stfdem8 -0.128185463641 0.089093739295 -1.439 0.150234
## stfdem9 -0.181924411182 0.099802289140 -1.823 0.068344 .
## stfdem10 -0.108168932476 0.114631143533 -0.944 0.345374
## gndr2 -0.095522183947 0.027188933002 -3.513 0.000444 ***
## agea 0.003944283840 0.001081100371 3.648 0.000265 ***
## rlgdgr1 0.052459426455 0.056206967531 0.933 0.350665
## rlgdgr2 0.022959018268 0.053383550864 0.430 0.667145
## rlgdgr3 0.125236929684 0.051573759421 2.428 0.015180 *
## rlgdgr4 0.091105132921 0.058134458505 1.567 0.117099
## rlgdgr5 0.000894788710 0.046968759233 0.019 0.984801
## rlgdgr6 0.059934454225 0.051558090456 1.162 0.245063
## rlgdgr7 -0.007731683715 0.051399015186 -0.150 0.880431
## rlgdgr8 0.007929514283 0.055342018474 0.143 0.886069
## rlgdgr9 -0.075097836124 0.085721803787 -0.876 0.381007
## rlgdgr10 0.175815400780 0.068045281012 2.584 0.009780 **
## yrpy -0.000000008538 0.000000007710 -1.107 0.268132
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for quasipoisson family taken to be 206.3327)
##
## Null deviance: 1622523 on 17028 degrees of freedom
## Residual deviance: 1532064 on 16960 degrees of freedom
## AIC: NA
##
## Number of Fisher Scoring iterations: 6## Analysis of Deviance Table
##
## Model 1: nwspol ~ polintr + eisced + trstprl + eduyrs + netusoft + stfeco +
## stfgov + stfdem + gndr + agea + rlgdgr + yrpy
## Model 2: nwspol ~ polintr + eisced + trstprl + eduyrs + netusoft + stfeco +
## stfgov + stfdem + agea + rlgdgr + yrpy
## Resid. Df Resid. Dev Df Deviance F Pr(>F)
## 1 16960 1532064
## 2 16961 1534617 -1 -2552.4 12.37 0.0004373 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Yes, Gender plays a role for the quasi poisson model.
## Analysis of Deviance Table
##
## Model 1: nwspol ~ polintr + eisced + trstprl + eduyrs + netusoft + stfeco +
## stfgov + stfdem + gndr + agea + rlgdgr + yrpy
## Model 2: nwspol ~ polintr + eisced + trstprl + eduyrs + netusoft + stfeco +
## stfgov + gndr + agea + rlgdgr + yrpy
## Resid. Df Resid. Dev Df Deviance F Pr(>F)
## 1 16960 1532064
## 2 16970 1534358 -10 -2293.3 1.1114 0.3488To compare it with the variable of “stfdem” about how satisfied the participants of the survey are about how the democracy in their country works. This variable doensn’t play a role to predict the media consumption according to the quasipoisson model.
3.3 GLM Binomial
## tibble [2,212 × 11] (S3: tbl_df/tbl/data.frame)
## $ postonline : num [1:2212] 0 0 0 0 1 0 0 0 0 0 ...
## $ age.na : num [1:2212] 26 65 74 64 54 20 71 41 62 65 ...
## $ interest.fac: Ord.factor w/ 4 levels "none at all"<..: 2 4 4 4 2 3 4 2 3 3 ...
## $ gov.allows : Factor w/ 5 levels "1","2","3","4",..: 4 4 2 3 2 3 3 3 2 3 ...
## $ trustparl : num [1:2212] 2 7 3 3 4 9 10 5 5 7 ...
## $ polscale.na : num [1:2212] 5 5 3 2 1 5 5 2 3 5 ...
## $ eduyrs.na : num [1:2212] 13 11 20 12 14 13 12 14 16 14 ...
## $ ability.fac : Factor w/ 5 levels "not at all","a little",..: 3 4 3 3 4 4 3 2 4 3 ...
## $ sat.dem.fac : Factor w/ 11 levels "0","1","2","3",..: 8 10 7 5 6 11 11 8 7 10 ...
## $ boycott.fac : Factor w/ 2 levels "1","2": 2 1 2 1 1 2 1 2 1 2 ...
## $ vote.fac : Factor w/ 3 levels "Yes","No","Not eligible": 2 1 1 1 1 1 1 1 1 1 ...Graphical Analysis
Fitting the binary model (logistic regression
##
## Call:
## glm(formula = postonline ~ age.na, family = "binomial", data = ess9_binary.na)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -0.9792 -0.7141 -0.5696 -0.4445 2.1626
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -0.069127 0.140640 -0.492 0.623
## age.na -0.027793 0.002954 -9.408 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 2232.0 on 2211 degrees of freedom
## Residual deviance: 2138.1 on 2210 degrees of freedom
## AIC: 2142.1
##
## Number of Fisher Scoring iterations: 4##
## Call:
## glm(formula = postonline ~ age.na + eduyrs.na + interest.fac,
## family = "binomial", data = ess9_binary.na)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -1.3493 -0.7204 -0.5271 -0.3221 2.7383
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -0.623148 0.292110 -2.133 0.03290 *
## age.na -0.038233 0.003369 -11.349 < 0.0000000000000002 ***
## eduyrs.na 0.052708 0.016123 3.269 0.00108 **
## interest.fac.L 1.344213 0.288990 4.651 0.0000033 ***
## interest.fac.Q 0.100668 0.221055 0.455 0.64882
## interest.fac.C 0.005344 0.132833 0.040 0.96791
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 2232.0 on 2211 degrees of freedom
## Residual deviance: 2041.4 on 2206 degrees of freedom
## AIC: 2053.4
##
## Number of Fisher Scoring iterations: 5Estimating the performance of a binary model
## 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16
## 0.31 0.13 0.11 0.14 0.17 0.35 0.11 0.23 0.14 0.13 0.20 0.13 0.20 0.12 0.15 0.14
## 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32
## 0.18 0.11 0.13 0.22 0.10 0.31 0.09 0.31 0.12 0.28 0.18 0.14 0.10 0.12 0.23 0.08
## 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48
## 0.34 0.19 0.13 0.18 0.34 0.11 0.12 0.19 0.10 0.29 0.33 0.12 0.17 0.09 0.11 0.09
## 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64
## 0.31 0.35 0.24 0.34 0.12 0.16 0.19 0.16 0.19 0.23 0.13 0.29 0.35 0.13 0.15 0.12
## 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80
## 0.36 0.34 0.17 0.07 0.07 0.12 0.16 0.13 0.34 0.16 0.34 0.14 0.09 0.22 0.13 0.13
## 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96
## 0.27 0.19 0.09 0.25 0.12 0.31 0.22 0.16 0.37 0.36 0.14 0.26 0.34 0.09 0.14 0.17
## 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112
## 0.37 0.16 0.10 0.26 0.22 0.18 0.16 0.38 0.17 0.13 0.14 0.22 0.14 0.16 0.13 0.35
## 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128
## 0.18 0.18 0.21 0.28 0.13 0.19 0.35 0.19 0.20 0.26 0.16 0.26 0.37 0.21 0.09 0.14
## 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144
## 0.14 0.35 0.15 0.14 0.38 0.16 0.12 0.18 0.14 0.15 0.19 0.28 0.19 0.37 0.10 0.16
## 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160
## 0.09 0.13 0.28 0.09 0.31 0.20 0.28 0.21 0.17 0.26 0.24 0.14 0.12 0.21 0.21 0.18
## 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176
## 0.15 0.18 0.33 0.37 0.09 0.32 0.15 0.35 0.37 0.27 0.18 0.32 0.26 0.07 0.14 0.16
## 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192
## 0.16 0.23 0.16 0.20 0.23 0.19 0.15 0.31 0.23 0.16 0.16 0.25 0.21 0.22 0.14 0.29
## 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208
## 0.20 0.11 0.27 0.16 0.20 0.13 0.20 0.15 0.10 0.08 0.18 0.14 0.29 0.12 0.17 0.10
## 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224
## 0.37 0.13 0.08 0.35 0.14 0.21 0.16 0.22 0.25 0.24 0.14 0.22 0.10 0.14 0.21 0.12
## 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240
## 0.33 0.28 0.08 0.16 0.10 0.33 0.37 0.18 0.26 0.20 0.12 0.24 0.34 0.37 0.19 0.32
## 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256
## 0.13 0.26 0.07 0.35 0.12 0.20 0.18 0.15 0.11 0.09 0.22 0.34 0.26 0.26 0.10 0.15
## 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272
## 0.34 0.11 0.25 0.25 0.17 0.13 0.25 0.18 0.25 0.27 0.31 0.20 0.29 0.27 0.09 0.11
## 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288
## 0.22 0.25 0.31 0.14 0.18 0.32 0.11 0.09 0.19 0.32 0.17 0.31 0.14 0.34 0.09 0.21
## 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304
## 0.16 0.29 0.10 0.27 0.15 0.13 0.14 0.25 0.08 0.22 0.32 0.15 0.21 0.18 0.21 0.37
## 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320
## 0.21 0.12 0.29 0.26 0.15 0.24 0.18 0.20 0.17 0.11 0.15 0.12 0.10 0.23 0.16 0.28
## 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336
## 0.18 0.15 0.16 0.09 0.17 0.14 0.17 0.16 0.21 0.37 0.09 0.25 0.09 0.23 0.14 0.20
## 337 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352
## 0.09 0.25 0.11 0.28 0.11 0.14 0.29 0.16 0.12 0.14 0.19 0.27 0.17 0.23 0.19 0.18
## 353 354 355 356 357 358 359 360 361 362 363 364 365 366 367 368
## 0.14 0.12 0.34 0.12 0.37 0.15 0.09 0.14 0.17 0.14 0.32 0.08 0.20 0.18 0.36 0.30
## 369 370 371 372 373 374 375 376 377 378 379 380 381 382 383 384
## 0.28 0.19 0.17 0.27 0.25 0.15 0.23 0.19 0.37 0.10 0.14 0.13 0.35 0.11 0.22 0.33
## 385 386 387 388 389 390 391 392 393 394 395 396 397 398 399 400
## 0.31 0.25 0.18 0.09 0.18 0.34 0.16 0.28 0.29 0.15 0.17 0.20 0.13 0.34 0.12 0.15
## 401 402 403 404 405 406 407 408 409 410 411 412 413 414 415 416
## 0.27 0.11 0.23 0.22 0.14 0.19 0.14 0.18 0.10 0.32 0.13 0.15 0.10 0.26 0.08 0.13
## 417 418 419 420 421 422 423 424 425 426 427 428 429 430 431 432
## 0.08 0.33 0.21 0.11 0.23 0.16 0.35 0.27 0.20 0.12 0.14 0.20 0.31 0.13 0.12 0.31
## 433 434 435 436 437 438 439 440 441 442 443 444 445 446 447 448
## 0.37 0.17 0.09 0.14 0.18 0.18 0.14 0.12 0.19 0.10 0.34 0.10 0.15 0.16 0.10 0.21
## 449 450 451 452 453 454 455 456 457 458 459 460 461 462 463 464
## 0.27 0.20 0.17 0.17 0.34 0.27 0.28 0.18 0.11 0.09 0.13 0.11 0.14 0.19 0.29 0.33
## 465 466 467 468 469 470 471 472 473 474 475 476 477 478 479 480
## 0.23 0.15 0.10 0.16 0.09 0.16 0.16 0.24 0.18 0.15 0.18 0.23 0.13 0.12 0.15 0.23
## 481 482 483 484 485 486 487 488 489 490 491 492 493 494 495 496
## 0.15 0.16 0.14 0.32 0.38 0.29 0.21 0.31 0.18 0.15 0.29 0.16 0.26 0.21 0.29 0.22
## 497 498 499 500 501 502 503 504 505 506 507 508 509 510 511 512
## 0.25 0.12 0.20 0.07 0.16 0.34 0.10 0.23 0.23 0.18 0.28 0.09 0.13 0.10 0.22 0.10
## 513 514 515 516 517 518 519 520 521 522 523 524 525 526 527 528
## 0.11 0.12 0.26 0.09 0.16 0.36 0.21 0.11 0.35 0.12 0.31 0.12 0.18 0.19 0.15 0.09
## 529 530 531 532 533 534 535 536 537 538 539 540 541 542 543 544
## 0.33 0.29 0.10 0.24 0.31 0.38 0.18 0.25 0.34 0.19 0.35 0.24 0.09 0.19 0.10 0.31
## 545 546 547 548 549 550 551 552 553 554 555 556 557 558 559 560
## 0.35 0.07 0.19 0.23 0.11 0.18 0.23 0.29 0.35 0.11 0.36 0.18 0.27 0.34 0.17 0.33
## 561 562 563 564 565 566 567 568 569 570 571 572 573 574 575 576
## 0.16 0.37 0.23 0.18 0.12 0.14 0.11 0.15 0.10 0.20 0.12 0.30 0.20 0.34 0.37 0.10
## 577 578 579 580 581 582 583 584 585 586 587 588 589 590 591 592
## 0.22 0.18 0.15 0.30 0.16 0.16 0.18 0.17 0.11 0.36 0.26 0.15 0.34 0.08 0.11 0.29
## 593 594 595 596 597 598 599 600 601 602 603 604 605 606 607 608
## 0.10 0.12 0.27 0.13 0.11 0.13 0.20 0.13 0.21 0.10 0.25 0.34 0.14 0.17 0.10 0.18
## 609 610 611 612 613 614 615 616 617 618 619 620 621 622 623 624
## 0.31 0.13 0.30 0.07 0.23 0.14 0.15 0.23 0.10 0.15 0.35 0.13 0.07 0.18 0.12 0.16
## 625 626 627 628 629 630 631 632 633 634 635 636 637 638 639 640
## 0.17 0.29 0.35 0.15 0.18 0.18 0.29 0.25 0.23 0.18 0.15 0.31 0.30 0.34 0.16 0.21
## 641 642 643 644 645 646 647 648 649 650 651 652 653 654 655 656
## 0.09 0.14 0.18 0.20 0.13 0.12 0.28 0.11 0.18 0.34 0.35 0.20 0.14 0.37 0.34 0.19
## 657 658 659 660 661 662 663 664 665 666 667 668 669 670 671 672
## 0.13 0.13 0.17 0.37 0.10 0.14 0.25 0.14 0.16 0.26 0.28 0.23 0.37 0.36 0.26 0.20
## 673 674 675 676 677 678 679 680 681 682 683 684 685 686 687 688
## 0.20 0.23 0.29 0.17 0.11 0.34 0.15 0.12 0.30 0.14 0.13 0.10 0.14 0.30 0.32 0.32
## 689 690 691 692 693 694 695 696 697 698 699 700 701 702 703 704
## 0.37 0.25 0.14 0.24 0.28 0.16 0.28 0.18 0.20 0.11 0.34 0.20 0.14 0.19 0.34 0.28
## 705 706 707 708 709 710 711 712 713 714 715 716 717 718 719 720
## 0.14 0.13 0.20 0.15 0.25 0.22 0.18 0.22 0.26 0.16 0.14 0.17 0.27 0.17 0.30 0.10
## 721 722 723 724 725 726 727 728 729 730 731 732 733 734 735 736
## 0.10 0.13 0.37 0.15 0.31 0.12 0.13 0.23 0.12 0.33 0.30 0.18 0.16 0.16 0.22 0.13
## 737 738 739 740 741 742 743 744 745 746 747 748 749 750 751 752
## 0.23 0.09 0.35 0.35 0.12 0.24 0.34 0.11 0.15 0.14 0.21 0.17 0.19 0.35 0.18 0.28
## 753 754 755 756 757 758 759 760 761 762 763 764 765 766 767 768
## 0.09 0.21 0.20 0.35 0.34 0.24 0.31 0.15 0.10 0.14 0.11 0.12 0.18 0.12 0.35 0.26
## 769 770 771 772 773 774 775 776 777 778 779 780 781 782 783 784
## 0.32 0.25 0.32 0.21 0.28 0.13 0.22 0.18 0.37 0.09 0.30 0.14 0.10 0.10 0.34 0.17
## 785 786 787 788 789 790 791 792 793 794 795 796 797 798 799 800
## 0.21 0.27 0.08 0.38 0.31 0.23 0.14 0.26 0.12 0.27 0.24 0.09 0.24 0.19 0.18 0.16
## 801 802 803 804 805 806 807 808 809 810 811 812 813 814 815 816
## 0.12 0.15 0.12 0.23 0.20 0.13 0.23 0.18 0.20 0.18 0.32 0.36 0.12 0.18 0.29 0.18
## 817 818 819 820 821 822 823 824 825 826 827 828 829 830 831 832
## 0.14 0.28 0.28 0.28 0.18 0.11 0.16 0.16 0.26 0.14 0.35 0.21 0.17 0.35 0.16 0.23
## 833 834 835 836 837 838 839 840 841 842 843 844 845 846 847 848
## 0.21 0.25 0.21 0.21 0.23 0.32 0.08 0.29 0.11 0.30 0.22 0.32 0.31 0.20 0.19 0.11
## 849 850 851 852 853 854 855 856 857 858 859 860 861 862 863 864
## 0.34 0.17 0.33 0.36 0.18 0.18 0.18 0.35 0.27 0.15 0.12 0.38 0.22 0.17 0.22 0.09
## 865 866 867 868 869 870 871 872 873 874 875 876 877 878 879 880
## 0.09 0.37 0.17 0.27 0.14 0.10 0.32 0.37 0.18 0.16 0.20 0.23 0.12 0.11 0.20 0.08
## 881 882 883 884 885 886 887 888 889 890 891 892 893 894 895 896
## 0.08 0.13 0.09 0.22 0.14 0.35 0.11 0.23 0.10 0.19 0.18 0.22 0.12 0.31 0.29 0.33
## 897 898 899 900 901 902 903 904 905 906 907 908 909 910 911 912
## 0.36 0.26 0.16 0.32 0.29 0.20 0.32 0.14 0.30 0.12 0.31 0.12 0.11 0.24 0.16 0.12
## 913 914 915 916 917 918 919 920 921 922 923 924 925 926 927 928
## 0.16 0.13 0.18 0.19 0.31 0.35 0.23 0.14 0.12 0.28 0.28 0.11 0.21 0.08 0.21 0.25
## 929 930 931 932 933 934 935 936 937 938 939 940 941 942 943 944
## 0.13 0.18 0.19 0.19 0.09 0.12 0.33 0.25 0.19 0.14 0.09 0.28 0.30 0.14 0.21 0.07
## 945 946 947 948 949 950 951 952 953 954 955 956 957 958 959 960
## 0.18 0.37 0.37 0.22 0.35 0.15 0.14 0.15 0.11 0.15 0.15 0.15 0.18 0.14 0.27 0.16
## 961 962 963 964 965 966 967 968 969 970 971 972 973 974 975 976
## 0.10 0.14 0.14 0.09 0.14 0.17 0.15 0.13 0.19 0.13 0.16 0.26 0.25 0.26 0.35 0.15
## 977 978 979 980 981 982 983 984 985 986 987 988 989 990 991 992
## 0.17 0.18 0.35 0.14 0.20 0.11 0.15 0.08 0.26 0.11 0.26 0.15 0.33 0.25 0.37 0.14
## 993 994 995 996 997 998 999 1000 1001 1002 1003 1004 1005 1006 1007 1008
## 0.36 0.30 0.12 0.16 0.12 0.09 0.10 0.35 0.20 0.35 0.13 0.31 0.20 0.23 0.23 0.26
## 1009 1010 1011 1012 1013 1014 1015 1016 1017 1018 1019 1020 1021 1022 1023 1024
## 0.13 0.23 0.15 0.20 0.37 0.14 0.13 0.14 0.18 0.21 0.12 0.14 0.16 0.25 0.31 0.18
## 1025 1026 1027 1028 1029 1030 1031 1032 1033 1034 1035 1036 1037 1038 1039 1040
## 0.13 0.10 0.37 0.26 0.22 0.15 0.23 0.14 0.17 0.33 0.13 0.29 0.10 0.18 0.33 0.11
## 1041 1042 1043 1044 1045 1046 1047 1048 1049 1050 1051 1052 1053 1054 1055 1056
## 0.28 0.32 0.24 0.12 0.15 0.09 0.13 0.37 0.13 0.30 0.20 0.16 0.24 0.15 0.17 0.07
## 1057 1058 1059 1060 1061 1062 1063 1064 1065 1066 1067 1068 1069 1070 1071 1072
## 0.28 0.13 0.22 0.14 0.26 0.26 0.18 0.35 0.26 0.10 0.20 0.13 0.07 0.32 0.32 0.17
## 1073 1074 1075 1076 1077 1078 1079 1080 1081 1082 1083 1084 1085 1086 1087 1088
## 0.34 0.28 0.37 0.18 0.19 0.25 0.14 0.19 0.12 0.16 0.30 0.26 0.10 0.20 0.18 0.10
## 1089 1090 1091 1092 1093 1094 1095 1096 1097 1098 1099 1100 1101 1102 1103 1104
## 0.34 0.18 0.13 0.12 0.37 0.22 0.26 0.34 0.16 0.13 0.18 0.15 0.32 0.13 0.18 0.10
## 1105 1106 1107 1108 1109 1110 1111 1112 1113 1114 1115 1116 1117 1118 1119 1120
## 0.13 0.37 0.08 0.15 0.14 0.32 0.35 0.08 0.27 0.23 0.16 0.20 0.20 0.10 0.19 0.31
## 1121 1122 1123 1124 1125 1126 1127 1128 1129 1130 1131 1132 1133 1134 1135 1136
## 0.33 0.34 0.16 0.22 0.10 0.22 0.14 0.29 0.22 0.15 0.36 0.35 0.22 0.20 0.18 0.18
## 1137 1138 1139 1140 1141 1142 1143 1144 1145 1146 1147 1148 1149 1150 1151 1152
## 0.17 0.14 0.31 0.13 0.20 0.19 0.23 0.28 0.18 0.11 0.26 0.16 0.33 0.09 0.15 0.09
## 1153 1154 1155 1156 1157 1158 1159 1160 1161 1162 1163 1164 1165 1166 1167 1168
## 0.15 0.14 0.27 0.33 0.15 0.14 0.11 0.14 0.25 0.10 0.11 0.18 0.14 0.14 0.23 0.35
## 1169 1170 1171 1172 1173 1174 1175 1176 1177 1178 1179 1180 1181 1182 1183 1184
## 0.18 0.18 0.10 0.29 0.31 0.37 0.11 0.26 0.37 0.18 0.13 0.26 0.17 0.17 0.38 0.10
## 1185 1186 1187 1188 1189 1190 1191 1192 1193 1194 1195 1196 1197 1198 1199 1200
## 0.18 0.16 0.22 0.34 0.07 0.10 0.08 0.37 0.12 0.16 0.21 0.17 0.09 0.37 0.37 0.33
## 1201 1202 1203 1204 1205 1206 1207 1208 1209 1210 1211 1212 1213 1214 1215 1216
## 0.35 0.16 0.18 0.12 0.29 0.23 0.36 0.11 0.36 0.10 0.14 0.14 0.29 0.12 0.32 0.31
## 1217 1218 1219 1220 1221 1222 1223 1224 1225 1226 1227 1228 1229 1230 1231 1232
## 0.13 0.35 0.18 0.21 0.15 0.29 0.31 0.13 0.15 0.11 0.11 0.19 0.16 0.14 0.18 0.34
## 1233 1234 1235 1236 1237 1238 1239 1240 1241 1242 1243 1244 1245 1246 1247 1248
## 0.17 0.11 0.25 0.29 0.15 0.20 0.18 0.22 0.25 0.18 0.37 0.33 0.10 0.15 0.29 0.30
## 1249 1250 1251 1252 1253 1254 1255 1256 1257 1258 1259 1260 1261 1262 1263 1264
## 0.31 0.09 0.10 0.23 0.20 0.15 0.13 0.09 0.13 0.10 0.23 0.10 0.37 0.30 0.23 0.12
## 1265 1266 1267 1268 1269 1270 1271 1272 1273 1274 1275 1276 1277 1278 1279 1280
## 0.09 0.29 0.28 0.10 0.13 0.26 0.11 0.11 0.29 0.13 0.18 0.15 0.18 0.35 0.22 0.20
## 1281 1282 1283 1284 1285 1286 1287 1288 1289 1290 1291 1292 1293 1294 1295 1296
## 0.29 0.38 0.15 0.14 0.11 0.15 0.19 0.15 0.36 0.26 0.12 0.19 0.23 0.30 0.10 0.09
## 1297 1298 1299 1300 1301 1302 1303 1304 1305 1306 1307 1308 1309 1310 1311 1312
## 0.27 0.29 0.25 0.31 0.37 0.14 0.12 0.09 0.13 0.26 0.12 0.18 0.22 0.37 0.19 0.38
## 1313 1314 1315 1316 1317 1318 1319 1320 1321 1322 1323 1324 1325 1326 1327 1328
## 0.19 0.11 0.34 0.21 0.28 0.12 0.28 0.18 0.29 0.30 0.34 0.29 0.29 0.12 0.16 0.27
## 1329 1330 1331 1332 1333 1334 1335 1336 1337 1338 1339 1340 1341 1342 1343 1344
## 0.12 0.17 0.13 0.15 0.16 0.19 0.18 0.27 0.29 0.28 0.13 0.13 0.29 0.15 0.21 0.11
## 1345 1346 1347 1348 1349 1350 1351 1352 1353 1354 1355 1356 1357 1358 1359 1360
## 0.25 0.15 0.08 0.24 0.21 0.13 0.27 0.12 0.23 0.18 0.09 0.19 0.26 0.09 0.29 0.17
## 1361 1362 1363 1364 1365 1366 1367 1368 1369 1370 1371 1372 1373 1374 1375 1376
## 0.15 0.37 0.34 0.23 0.23 0.21 0.17 0.09 0.16 0.26 0.27 0.38 0.28 0.19 0.23 0.18
## 1377 1378 1379 1380 1381 1382 1383 1384 1385 1386 1387 1388 1389 1390 1391 1392
## 0.15 0.13 0.21 0.20 0.23 0.15 0.29 0.31 0.12 0.21 0.10 0.25 0.18 0.14 0.15 0.22
## 1393 1394 1395 1396 1397 1398 1399 1400 1401 1402 1403 1404 1405 1406 1407 1408
## 0.19 0.09 0.32 0.10 0.37 0.17 0.10 0.37 0.30 0.36 0.27 0.18 0.17 0.15 0.13 0.13
## 1409 1410 1411 1412 1413 1414 1415 1416 1417 1418 1419 1420 1421 1422 1423 1424
## 0.29 0.17 0.32 0.11 0.25 0.12 0.15 0.19 0.20 0.16 0.12 0.22 0.21 0.12 0.15 0.13
## 1425 1426 1427 1428 1429 1430 1431 1432 1433 1434 1435 1436 1437 1438 1439 1440
## 0.11 0.14 0.34 0.13 0.14 0.08 0.10 0.28 0.13 0.24 0.13 0.21 0.17 0.15 0.33 0.36
## 1441 1442 1443 1444 1445 1446 1447 1448 1449 1450 1451 1452 1453 1454 1455 1456
## 0.37 0.20 0.16 0.26 0.13 0.29 0.29 0.17 0.21 0.14 0.11 0.31 0.11 0.37 0.26 0.16
## 1457 1458 1459 1460 1461 1462 1463 1464 1465 1466 1467 1468 1469 1470 1471 1472
## 0.34 0.23 0.10 0.31 0.17 0.37 0.29 0.13 0.15 0.19 0.12 0.13 0.22 0.10 0.18 0.16
## 1473 1474 1475 1476 1477 1478 1479 1480 1481 1482 1483 1484 1485 1486 1487 1488
## 0.11 0.21 0.10 0.25 0.20 0.17 0.14 0.15 0.37 0.11 0.16 0.34 0.25 0.14 0.34 0.15
## 1489 1490 1491 1492 1493 1494 1495 1496 1497 1498 1499 1500 1501 1502 1503 1504
## 0.22 0.34 0.15 0.22 0.16 0.13 0.28 0.13 0.36 0.15 0.23 0.35 0.28 0.17 0.32 0.12
## 1505 1506 1507 1508 1509 1510 1511 1512 1513 1514 1515 1516 1517 1518 1519 1520
## 0.14 0.33 0.23 0.26 0.27 0.25 0.09 0.33 0.33 0.23 0.23 0.12 0.35 0.15 0.24 0.20
## 1521 1522 1523 1524 1525 1526 1527 1528 1529 1530 1531 1532 1533 1534 1535 1536
## 0.35 0.09 0.19 0.11 0.28 0.13 0.19 0.29 0.23 0.35 0.18 0.37 0.18 0.16 0.19 0.23
## 1537 1538 1539 1540 1541 1542 1543 1544 1545 1546 1547 1548 1549 1550 1551 1552
## 0.23 0.14 0.18 0.12 0.13 0.10 0.29 0.23 0.14 0.31 0.25 0.17 0.14 0.10 0.18 0.13
## 1553 1554 1555 1556 1557 1558 1559 1560 1561 1562 1563 1564 1565 1566 1567 1568
## 0.16 0.31 0.28 0.30 0.16 0.19 0.10 0.16 0.14 0.35 0.12 0.27 0.20 0.18 0.27 0.27
## 1569 1570 1571 1572 1573 1574 1575 1576 1577 1578 1579 1580 1581 1582 1583 1584
## 0.07 0.16 0.10 0.35 0.25 0.14 0.35 0.34 0.21 0.37 0.14 0.32 0.18 0.11 0.29 0.10
## 1585 1586 1587 1588 1589 1590 1591 1592 1593 1594 1595 1596 1597 1598 1599 1600
## 0.32 0.33 0.32 0.07 0.32 0.08 0.27 0.21 0.17 0.27 0.13 0.32 0.16 0.18 0.29 0.14
## 1601 1602 1603 1604 1605 1606 1607 1608 1609 1610 1611 1612 1613 1614 1615 1616
## 0.08 0.16 0.21 0.29 0.28 0.27 0.27 0.22 0.28 0.10 0.21 0.11 0.13 0.18 0.15 0.13
## 1617 1618 1619 1620 1621 1622 1623 1624 1625 1626 1627 1628 1629 1630 1631 1632
## 0.14 0.22 0.32 0.11 0.18 0.17 0.10 0.29 0.28 0.11 0.31 0.20 0.18 0.14 0.12 0.14
## 1633 1634 1635 1636 1637 1638 1639 1640 1641 1642 1643 1644 1645 1646 1647 1648
## 0.31 0.11 0.17 0.11 0.23 0.10 0.17 0.35 0.16 0.24 0.13 0.24 0.29 0.23 0.14 0.30
## 1649 1650 1651 1652 1653 1654 1655 1656 1657 1658 1659 1660 1661 1662 1663 1664
## 0.26 0.11 0.24 0.09 0.16 0.30 0.14 0.22 0.28 0.13 0.14 0.18 0.08 0.31 0.17 0.37
## 1665 1666 1667 1668 1669 1670 1671 1672 1673 1674 1675 1676 1677 1678 1679 1680
## 0.09 0.09 0.22 0.27 0.15 0.09 0.18 0.35 0.15 0.16 0.30 0.10 0.19 0.20 0.32 0.10
## 1681 1682 1683 1684 1685 1686 1687 1688 1689 1690 1691 1692 1693 1694 1695 1696
## 0.16 0.20 0.18 0.19 0.21 0.32 0.25 0.14 0.32 0.15 0.29 0.38 0.11 0.32 0.21 0.17
## 1697 1698 1699 1700 1701 1702 1703 1704 1705 1706 1707 1708 1709 1710 1711 1712
## 0.20 0.13 0.14 0.10 0.20 0.32 0.34 0.26 0.15 0.11 0.18 0.10 0.19 0.30 0.27 0.22
## 1713 1714 1715 1716 1717 1718 1719 1720 1721 1722 1723 1724 1725 1726 1727 1728
## 0.21 0.18 0.15 0.23 0.29 0.21 0.18 0.10 0.17 0.09 0.34 0.10 0.32 0.14 0.14 0.15
## 1729 1730 1731 1732 1733 1734 1735 1736 1737 1738 1739 1740 1741 1742 1743 1744
## 0.20 0.24 0.09 0.20 0.38 0.10 0.13 0.37 0.23 0.14 0.13 0.15 0.14 0.17 0.11 0.34
## 1745 1746 1747 1748 1749 1750 1751 1752 1753 1754 1755 1756 1757 1758 1759 1760
## 0.18 0.15 0.09 0.15 0.10 0.08 0.13 0.16 0.14 0.31 0.29 0.17 0.26 0.25 0.07 0.15
## 1761 1762 1763 1764 1765 1766 1767 1768 1769 1770 1771 1772 1773 1774 1775 1776
## 0.27 0.20 0.37 0.29 0.27 0.09 0.15 0.19 0.11 0.13 0.24 0.10 0.17 0.18 0.18 0.19
## 1777 1778 1779 1780 1781 1782 1783 1784 1785 1786 1787 1788 1789 1790 1791 1792
## 0.31 0.33 0.12 0.11 0.15 0.16 0.23 0.33 0.13 0.31 0.26 0.15 0.19 0.12 0.35 0.19
## 1793 1794 1795 1796 1797 1798 1799 1800 1801 1802 1803 1804 1805 1806 1807 1808
## 0.24 0.14 0.11 0.15 0.14 0.16 0.17 0.17 0.29 0.09 0.35 0.16 0.19 0.20 0.21 0.10
## 1809 1810 1811 1812 1813 1814 1815 1816 1817 1818 1819 1820 1821 1822 1823 1824
## 0.14 0.20 0.13 0.27 0.29 0.12 0.16 0.14 0.08 0.25 0.31 0.36 0.15 0.15 0.21 0.15
## 1825 1826 1827 1828 1829 1830 1831 1832 1833 1834 1835 1836 1837 1838 1839 1840
## 0.09 0.14 0.16 0.22 0.23 0.35 0.18 0.18 0.09 0.25 0.09 0.15 0.28 0.20 0.27 0.18
## 1841 1842 1843 1844 1845 1846 1847 1848 1849 1850 1851 1852 1853 1854 1855 1856
## 0.34 0.21 0.25 0.09 0.26 0.28 0.38 0.28 0.22 0.26 0.09 0.27 0.36 0.27 0.17 0.28
## 1857 1858 1859 1860 1861 1862 1863 1864 1865 1866 1867 1868 1869 1870 1871 1872
## 0.15 0.23 0.16 0.14 0.16 0.17 0.21 0.26 0.18 0.15 0.10 0.20 0.15 0.12 0.36 0.14
## 1873 1874 1875 1876 1877 1878 1879 1880 1881 1882 1883 1884 1885 1886 1887 1888
## 0.24 0.34 0.16 0.11 0.14 0.14 0.17 0.34 0.17 0.14 0.17 0.31 0.31 0.34 0.12 0.11
## 1889 1890 1891 1892 1893 1894 1895 1896 1897 1898 1899 1900 1901 1902 1903 1904
## 0.16 0.33 0.21 0.31 0.29 0.11 0.12 0.13 0.15 0.33 0.10 0.16 0.38 0.38 0.19 0.34
## 1905 1906 1907 1908 1909 1910 1911 1912 1913 1914 1915 1916 1917 1918 1919 1920
## 0.16 0.37 0.26 0.10 0.17 0.21 0.35 0.13 0.21 0.11 0.11 0.10 0.24 0.14 0.28 0.22
## 1921 1922 1923 1924 1925 1926 1927 1928 1929 1930 1931 1932 1933 1934 1935 1936
## 0.28 0.32 0.32 0.18 0.28 0.08 0.17 0.21 0.07 0.32 0.16 0.34 0.15 0.26 0.13 0.09
## 1937 1938 1939 1940 1941 1942 1943 1944 1945 1946 1947 1948 1949 1950 1951 1952
## 0.32 0.14 0.12 0.23 0.31 0.27 0.21 0.11 0.36 0.36 0.16 0.13 0.14 0.14 0.17 0.08
## 1953 1954 1955 1956 1957 1958 1959 1960 1961 1962 1963 1964 1965 1966 1967 1968
## 0.29 0.10 0.12 0.12 0.15 0.18 0.27 0.11 0.22 0.23 0.13 0.16 0.13 0.35 0.25 0.25
## 1969 1970 1971 1972 1973 1974 1975 1976 1977 1978 1979 1980 1981 1982 1983 1984
## 0.17 0.13 0.16 0.09 0.36 0.12 0.27 0.07 0.16 0.14 0.26 0.10 0.22 0.27 0.36 0.17
## 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000
## 0.23 0.25 0.17 0.08 0.35 0.28 0.11 0.09 0.12 0.31 0.32 0.32 0.23 0.20 0.34 0.26
## 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016
## 0.16 0.09 0.27 0.12 0.14 0.11 0.14 0.23 0.10 0.15 0.34 0.36 0.30 0.14 0.20 0.19
## 2017 2018 2019 2020 2021 2022 2023 2024 2025 2026 2027 2028 2029 2030 2031 2032
## 0.15 0.25 0.17 0.09 0.35 0.26 0.27 0.20 0.15 0.35 0.21 0.16 0.09 0.14 0.13 0.10
## 2033 2034 2035 2036 2037 2038 2039 2040 2041 2042 2043 2044 2045 2046 2047 2048
## 0.12 0.27 0.13 0.35 0.24 0.14 0.21 0.29 0.10 0.14 0.20 0.17 0.36 0.23 0.25 0.14
## 2049 2050 2051 2052 2053 2054 2055 2056 2057 2058 2059 2060 2061 2062 2063 2064
## 0.29 0.26 0.17 0.15 0.25 0.18 0.15 0.33 0.11 0.28 0.14 0.12 0.27 0.11 0.23 0.14
## 2065 2066 2067 2068 2069 2070 2071 2072 2073 2074 2075 2076 2077 2078 2079 2080
## 0.13 0.27 0.32 0.20 0.21 0.19 0.18 0.13 0.37 0.18 0.28 0.16 0.35 0.08 0.34 0.16
## 2081 2082 2083 2084 2085 2086 2087 2088 2089 2090 2091 2092 2093 2094 2095 2096
## 0.19 0.12 0.22 0.36 0.36 0.26 0.18 0.19 0.34 0.10 0.28 0.22 0.10 0.11 0.17 0.15
## 2097 2098 2099 2100 2101 2102 2103 2104 2105 2106 2107 2108 2109 2110 2111 2112
## 0.34 0.12 0.19 0.18 0.12 0.15 0.10 0.36 0.14 0.34 0.34 0.29 0.14 0.28 0.11 0.16
## 2113 2114 2115 2116 2117 2118 2119 2120 2121 2122 2123 2124 2125 2126 2127 2128
## 0.37 0.19 0.16 0.29 0.19 0.37 0.11 0.11 0.14 0.11 0.08 0.12 0.30 0.27 0.34 0.23
## 2129 2130 2131 2132 2133 2134 2135 2136 2137 2138 2139 2140 2141 2142 2143 2144
## 0.18 0.17 0.22 0.27 0.29 0.23 0.26 0.12 0.32 0.10 0.18 0.34 0.19 0.22 0.22 0.14
## 2145 2146 2147 2148 2149 2150 2151 2152 2153 2154 2155 2156 2157 2158 2159 2160
## 0.14 0.27 0.18 0.27 0.18 0.13 0.12 0.27 0.15 0.19 0.14 0.29 0.24 0.32 0.20 0.19
## 2161 2162 2163 2164 2165 2166 2167 2168 2169 2170 2171 2172 2173 2174 2175 2176
## 0.19 0.34 0.17 0.25 0.17 0.23 0.09 0.17 0.17 0.12 0.30 0.28 0.29 0.16 0.34 0.16
## 2177 2178 2179 2180 2181 2182 2183 2184 2185 2186 2187 2188 2189 2190 2191 2192
## 0.27 0.14 0.23 0.27 0.22 0.12 0.35 0.07 0.11 0.11 0.19 0.18 0.09 0.13 0.11 0.13
## 2193 2194 2195 2196 2197 2198 2199 2200 2201 2202 2203 2204 2205 2206 2207 2208
## 0.22 0.22 0.13 0.37 0.22 0.16 0.19 0.18 0.10 0.27 0.19 0.28 0.07 0.11 0.19 0.11
## 2209 2210 2211 2212
## 0.18 0.26 0.22 0.26## 1 2 3 4 5 6
## 0 1 1 1 1 0## 0 1
## 363 1849## fitted
## obs 0 1
## 0 255 1508
## 1 108 3413.4 GAM
In this chapter a generalized additive model (GAM) is applied to the data set. First the data is visually analyzed and checked. The variables don’t show a strong relation ship and the line is mostly horizontal.
3.5 Neural Network
Additional to “classic” linear models and GLM options, there are neural networks for analyzing and predicting data.
3.5.1 Neural Network
Within this chapter two types of neural network model are applied to two different questions.
- Predicting the values for the continuous variable
- Predicting the vallues for the categorical variable inidicating the 5 levels of ### 3.5.1 continious variable
## tibble [17,029 × 14] (S3: tbl_df/tbl/data.frame)
## $ nwspol : num [1:17029] 60 45 60 120 15 60 30 30 10 10 ...
## $ polintr : num [1:17029] 4 3 4 2 4 1 2 3 2 2 ...
## $ trstprl : num [1:17029] 6 0 0 6 4 3 3 7 7 5 ...
## $ eisced : num [1:17029] 2 3 3 3 3 4 3 6 2 7 ...
## $ eduyrs : num [1:17029] 12 11 12 12 13 21 18 17 9 17 ...
## $ stfeco : num [1:17029] 5 6 1 10 9 7 6 7 6 8 ...
## $ stfgov : num [1:17029] 6 8 3 10 8 2 7 2 7 6 ...
## $ stfdem : num [1:17029] 6 6 3 10 7 3 10 6 8 7 ...
## $ gndr : num [1:17029] 2 1 1 1 2 1 1 1 1 1 ...
## $ agea : num [1:17029] 40 63 56 48 41 27 49 42 50 35 ...
## $ rlgdgr : num [1:17029] 4 1 8 0 3 3 2 0 3 2 ...
## $ netusoft: num [1:17029] 4 5 1 1 4 5 5 5 5 5 ...
## $ psppsgva: num [1:17029] 2 2 2 5 1 3 1 1 2 3 ...
## $ yrpy : num [1:17029] 31200 30600 18000 31200 37200 18000 20400 17400 45600 70000 ...Prepare for Training
Fit the Network
Predict the test set
So what went wrong here? Obviously we also need to use the scaled data to make predictions and then scale back to real nwspol values
And calculate the RMSE
## [1] 123.4398Redo with caret and Cross Validation
and extract the best model
3.5.2 Categorical variable
## tibble [17,029 × 14] (S3: tbl_df/tbl/data.frame)
## $ nwspol : num [1:17029] 60 45 60 120 15 60 30 30 10 10 ...
## $ polintr : num [1:17029] 4 3 4 2 4 1 2 3 2 2 ...
## $ trstprl : num [1:17029] 6 0 0 6 4 3 3 7 7 5 ...
## $ eisced : num [1:17029] 2 3 3 3 3 4 3 6 2 7 ...
## $ eduyrs : num [1:17029] 12 11 12 12 13 21 18 17 9 17 ...
## $ stfeco : num [1:17029] 5 6 1 10 9 7 6 7 6 8 ...
## $ stfgov : num [1:17029] 6 8 3 10 8 2 7 2 7 6 ...
## $ stfdem : num [1:17029] 6 6 3 10 7 3 10 6 8 7 ...
## $ gndr : num [1:17029] 2 1 1 1 2 1 1 1 1 1 ...
## $ agea : num [1:17029] 40 63 56 48 41 27 49 42 50 35 ...
## $ rlgdgr : num [1:17029] 4 1 8 0 3 3 2 0 3 2 ...
## $ netusoft: Factor w/ 5 levels "1","2","3","4",..: 4 5 1 1 4 5 5 5 5 5 ...
## $ psppsgva: num [1:17029] 2 2 2 5 1 3 1 1 2 3 ...
## $ yrpy : num [1:17029] 31200 30600 18000 31200 37200 18000 20400 17400 45600 70000 ...Have a quick look at the Data
Prepare the Data for Training
Why do we use the caret function
Look at the distribution of our different prediction classes in the train and test datasets:
## train
## netusoft FALSE TRUE
## 1 90 515
## 2 91 516
## 3 106 604
## 4 210 1193
## 5 2055 11649Now compare this to the “random” approach:
## train
## netusoft FALSE TRUE
## 1 104 501
## 2 89 518
## 3 106 604
## 4 197 1206
## 5 2033 11671So using the createDataPartition function makes sure
that no class is over- or underrepresented relative to the total
occurance in the two sets
Create some easy Variables to access Data
Train the Neural Network
Call the neuralnet function creating a network with two
hidden layers containing 4 and 3 neurons (probably way too complex for
our problem here).
Plot the resulting network including the weights
Make Predictions
Find class (i.e. output neuron) with the highest probability and convert this back into a factor
Evaluate the Results
## Confusion Matrix and Statistics
##
## Reference
## Prediction 1 2 3 4 5
## 1 1 0 0 0 89
## 2 0 0 0 0 91
## 3 0 0 0 0 106
## 4 0 0 0 0 210
## 5 0 0 0 0 2055
##
## Overall Statistics
##
## Accuracy : 0.8056
## 95% CI : (0.7897, 0.8208)
## No Information Rate : 0.9996
## P-Value [Acc > NIR] : 1
##
## Kappa : 0.0036
##
## Mcnemar's Test P-Value : NA
##
## Statistics by Class:
##
## Class: 1 Class: 2 Class: 3 Class: 4 Class: 5
## Sensitivity 1.0000000 NA NA NA 0.805566
## Specificity 0.9651117 0.96434 0.95846 0.91771 1.000000
## Pos Pred Value 0.0111111 NA NA NA 1.000000
## Neg Pred Value 1.0000000 NA NA NA 0.002012
## Prevalence 0.0003918 0.00000 0.00000 0.00000 0.999608
## Detection Rate 0.0003918 0.00000 0.00000 0.00000 0.805251
## Detection Prevalence 0.0352665 0.03566 0.04154 0.08229 0.805251
## Balanced Accuracy 0.9825559 NA NA NA 0.902783Optimize Network Structure
First we need to remodel the data due to some limitations of
caret
## netusoft netusoft2 netusoft3 netusoft4 netusoft5
## 1 1 0 0 1 0
## 2 1 0 0 0 1
## 3 1 0 0 0 0
## 4 1 0 0 0 0
## 5 1 0 0 1 0
## 6 1 0 0 0 1And have a look at the different models
And try out the best model
3.6 Support Vector Machine
Loading the Packages
We are going to use the e1071 package for this example
and also some helper functions from caret
## left center right NA's
## 987 1149 125 97## 0 1 2 3 4 5 6 7 8 9 10 NA's
## 95 64 186 340 302 826 198 125 86 14 25 97## [1] "integer"## left center right NA's
## 987 1149 125 97## tibble [1,089 × 7] (S3: tbl_df/tbl/data.frame)
## $ age.na : num [1:1089] 26 54 41 47 48 60 44 51 26 79 ...
## $ interest.fac: Ord.factor w/ 4 levels "none at all"<..: 2 2 2 3 4 4 3 3 2 3 ...
## $ scale_cat : Factor w/ 3 levels "left","center",..: 2 1 1 1 2 1 2 2 2 3 ...
## $ eduyrs.na : num [1:1089] 13 14 14 13 18 16 0 14 10 7 ...
## $ sat.dem.fac : Factor w/ 11 levels "0","1","2","3",..: 8 6 8 6 10 9 6 6 5 5 ...
## $ yearpay : num [1:1089] 288000 72000 19200 26400 72000 ...
## $ vote.fac : Factor w/ 3 levels "Yes","No","Not eligible": 2 1 1 1 1 1 2 1 1 1 ...Have a quick look at the Data
Prepare the Data for Training
Create some easy Variables to access Data
Train the Neural Network
##
## Call:
## svm(formula = scale_cat ~ ., data = train, kernel = "linear", cost = 10,
## scale = TRUE)
##
##
## Parameters:
## SVM-Type: C-classification
## SVM-Kernel: linear
## cost: 10
##
## Number of Support Vectors: 833
##
## ( 385 408 40 )
##
##
## Number of Classes: 3
##
## Levels:
## left center right
##
## Parameter tuning of 'svm':
##
## - sampling method: 10-fold cross validation
##
## - best parameters:
## cost
## 10
##
## - best performance: 0.4334777
##
## - Detailed performance results:
## cost error dispersion
## 1 0.001 0.4838430 0.04493581
## 2 0.010 0.4784064 0.04000725
## 3 0.100 0.4380309 0.03559308
## 4 1.000 0.4408087 0.03350829
## 5 5.000 0.4343952 0.04236453
## 6 10.000 0.4334777 0.04530840
## 7 100.000 0.4362300 0.04347618##
## Parameter tuning of 'svm':
##
## - sampling method: 10-fold cross validation
##
## - best parameters:
## cost
## 10
##
## - best performance: 0.4334777
##
## - Detailed performance results:
## cost error dispersion
## 1 0.001 0.4838430 0.04493581
## 2 0.010 0.4784064 0.04000725
## 3 0.100 0.4380309 0.03559308
## 4 1.000 0.4408087 0.03350829
## 5 5.000 0.4343952 0.04236453
## 6 10.000 0.4334777 0.04530840
## 7 100.000 0.4362300 0.04347618##
## Call:
## best.tune(METHOD = svm, train.x = scale_cat ~ ., data = d.ess9_supportvec,
## ranges = list(cost = c(0.001, 0.01, 0.1, 1, 5, 10, 100)), kernel = "linear")
##
##
## Parameters:
## SVM-Type: C-classification
## SVM-Kernel: linear
## cost: 10
##
## Number of Support Vectors: 984
##
## ( 485 452 47 )
##
##
## Number of Classes: 3
##
## Levels:
## left center rightMake Predictions
## test_pred
## left center right
## 68 95 0Evaluate the Results
## Confusion Matrix and Statistics
##
## Reference
## Prediction left center right
## left 35 32 1
## center 37 52 6
## right 0 0 0
##
## Overall Statistics
##
## Accuracy : 0.5337
## 95% CI : (0.4541, 0.6121)
## No Information Rate : 0.5153
## P-Value [Acc > NIR] : 0.3479
##
## Kappa : 0.0953
##
## Mcnemar's Test P-Value : 0.0612
##
## Statistics by Class:
##
## Class: left Class: center Class: right
## Sensitivity 0.4861 0.6190 0.00000
## Specificity 0.6374 0.4557 1.00000
## Pos Pred Value 0.5147 0.5474 NaN
## Neg Pred Value 0.6105 0.5294 0.95706
## Prevalence 0.4417 0.5153 0.04294
## Detection Rate 0.2147 0.3190 0.00000
## Detection Prevalence 0.4172 0.5828 0.00000
## Balanced Accuracy 0.5617 0.5374 0.50000Try again with other parameters
##
## Call:
## svm(formula = scale_cat ~ ., data = train, kernel = "radial", cost = 100000,
## scale = TRUE)
##
##
## Parameters:
## SVM-Type: C-classification
## SVM-Kernel: radial
## cost: 100000
##
## Number of Support Vectors: 661
##
## ( 300 322 39 )
##
##
## Number of Classes: 3
##
## Levels:
## left center right
## test_pred1
## left center right
## 85 67 11## Confusion Matrix and Statistics
##
## Reference
## Prediction left center right
## left 40 41 4
## center 29 36 2
## right 3 7 1
##
## Overall Statistics
##
## Accuracy : 0.4724
## 95% CI : (0.3938, 0.552)
## No Information Rate : 0.5153
## P-Value [Acc > NIR] : 0.8801
##
## Kappa : 0.0492
##
## Mcnemar's Test P-Value : 0.1734
##
## Statistics by Class:
##
## Class: left Class: center Class: right
## Sensitivity 0.5556 0.4286 0.142857
## Specificity 0.5055 0.6076 0.935897
## Pos Pred Value 0.4706 0.5373 0.090909
## Neg Pred Value 0.5897 0.5000 0.960526
## Prevalence 0.4417 0.5153 0.042945
## Detection Rate 0.2454 0.2209 0.006135
## Detection Prevalence 0.5215 0.4110 0.067485
## Balanced Accuracy 0.5305 0.5181 0.539377